Stricter regulations on personal data are causing a shift towards contextual advertising, where keywords are used to predict the topical congruence between ads and their surrounding media contexts - an alignment shown to enhance advertising effectiveness. Recent advances in AI, particularly large language models, have improved keyword extraction capabilities but also introduced concerns about computational cost. This study conducts a comparative, survey-based evaluation experiment of three prominent keyword extraction approaches, emphasising user-perceived accuracy and efficiency. Based on responses from 552 participants, the embedding-based approach emerges as the preferred method. The findings underscore the importance of human-in-the-loop evaluation in real-world settings.